
Different from the previous image editing method, we propose to generate images directly from the semantic labels to improve the attribute accuracy of the resulting image.

To enhance the attribute disentanglement of the backbone, we propose to further project the style vectors of the intermediate latent space to a combination of scaling factors.
Inspired by [1]}, we achieve this by solving the instance-level system of linear equations whose coefficient matrix is the eigenvectors of the mapping model of StyleGAN[2]}.

As shown in our experimental results, we achieve both higher attribute accuracy and greater image diversity compared to previous methods.
The work also shows that the generated images can have more accurate and richer attribute generation if equipped with a higher accuracy multi-attribute classifier.

